The headline result of this paper is that ICU acquired hypernatraemia is associated with a 40% increase in risk for hospital mortality, and it is an independent risk factor for death.

The more I thought about this paper though, the more confused I got (I also don’t think it’s the most readable!). This paper highlights in my mind the difference between association and causation. Patients with hypernatraemia are different to those without (table 2), but what accounts for the observed increase in mortality (given it’s not any of these covariates)? Or is it more plausible to say hypernatraemia per. se. causes the increase in mortality? (My feeling is that it’s not, maybe you disagree?)To what extent do you think it is fair to say that hypernatraemia is a marker of poor patient management and is avoidable? Is ‘poor care’ the missing link that explains the association? How do you quantify ‘poor care’ as an outcome? What markers give an indication of poor care? So read the paper, see what you think, and hopefully you won’t end up as confused as I am! I look forward to your comments…..

I think this paper is pretty impenetrable.....to the cynic these are two different groups. A bit like saying noradrenaline in high doses kills you just because the noradrenaline group have a higher mortality. I think we need a stats explanation......

On your secondary point about poor care.....well along with poor education I think that both good and poor care are hard to define. We think we know what they look like but the definition is very difficult, a bit like what makes a good doctor...

I look forward to a comments on the stats and more thoughts on what makes good and poor care

Mark

Reply

Deva

11/2/2014 12:35:50 pm

Good research question; a problem that needs investigating (and investigated a fair bit!)

I noted a few things in this paper:

1. 11000 + patients were excluded because they were not discharged (I don’t know what they mean by that – hard to guess because of my lack of familiarity with the US databases).

2. 79000+ patients excluded because of lack of APACHE scores.

That’s 90,000+ patients excluded. This selection bias is a practical problem when we deal with databases with incomplete data sets. At least they were honest and presented the flow diagram!

3. Could they have used Propensity scores to match the baseline variables? Their hypernatraemia group is smallish and the non-hypernatraemia group is mega sized. By performing propensity matching, they could have had two well-matched (but smaller) groups without baseline differences. They then could have compared the outcomes. Of course the analysis would have been different. They do mention adjusting for confounders in their multivariate models, but I feel matching at first would be better than adjusting in the model (our mind set is used to having two groups with perfectly matched baseline variables!). The statistical analysis seems ok.

4. A prospective observational study excluding patients who received hypertonic saline or mannitol would be better than doing a retrospective sensitivity analysis by excluding all cerebro vascular diseases in general. But this is a retrospective review. It is what it is.

This is simply an association like lactate and mortality.

On your point about quality of care, like Mark said, it is extremely difficult to measure.
Which is better quality – diuresing a patient and making them hypernatraemic, but weaning them from mechanical ventilation or being less aggressive with furosemide, not being able to wean and giving them a VAP?

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Pete

12/2/2014 02:49:54 am

Taking the more straightforward one first I'm a pretty firm believer that given ARDS is non cardiogenic pulmonary oedema, the only and best treatment is diuresis (and correction of the underlying cause). Having said that, I don't find people wean if they have a sodium above 150 (anecdotally), and use hypernatraemia as a cue to think about backing off with the diuresis.

Hypernatraemia as a marker of poor care could be not paying attention to a rising sodium, unintentionally getting the fluid balance wrong, avoidable iatrogenic sodium overload etc. I also accept however that critical care is not an exercise in keeping all variables between "normal limits".

I think the propensity scoring vs multivariate analysis is 'interesting' - we would as Deva points out expect (and mainly because we're more used to it) matched groups.

My thoughts on which to choose (by way of demonstrating that everyone should contribute to the comments even if they haven't a clue what they're talking about!) are:

Propensity matching assumes knowledge of which variables need to be matched. It could possibly be argued that given the literature is pretty tiny and mainly by the same authors, some of these variables may be unknown and therefore the multivariate approach may be more appropriate. The variables identified by this paper could be used for future matching, acting as a reference to strengthen future propensity scoring.

and

By matching the groups, some data would be lost (i.e. all those patients that don't match) - whether this is important in practical terms I don't know but as a researcher it must hurt a bit to let it go?!

I'm struggling to find a reference that explains the process of propensity scoring in terms I can follow - if anyone knows of one please let me know!

Reply

Lucy

18/2/2014 04:08:13 am

A difficult paper to unpick but I will try and make some comment, accepting that I have a limited clue what I am talking about Pete...

In response to previous comments, I'm not sure how propensity matching would influence the results here, but it would be interesting to see how 'matched' subjects, based on the co-variables and potential confounders identified by the authors, did affect things, accepting that it would not account for the unknown variables, given my understanding of how this statistical process works. Would it be a way of 'all things being (more) equal'? That's a phrase I noted in the introduction.. The results certainly suggest to me that all things are not very equal between the groups compared.

For instance, the hypernatraemic patients, by the studies definition, were seemingly a much sicker group at their baseline admission status (higher APACHE, more likely to be medical patients and not elective surgical patients etc) and I was left wondering, could their hypernatraemia not be seen as more of a marker of their disease severity than the cause of their mortality.. It would seem to me obvious that we should pay attention to a rising or high sodium, as clinicians, and do what we can to avoid it, in clinical context; we do certainly have a big role to play in fluid and electrolyte balance, for our ICCU patients who seem to be to be particularly vulnerable to any errors we make in this, given that by the nature they can't usually regulate these very well by themselves, but I have always been taught to 'treat the patient and not the numbers'. I'm not sure that this paper provides enough evidence to say that we should religiously try to force all our patient's sodiums into the normal range at all costs?

I'll never forget the case of one elderly gentleman with advanced dementia who was admitted to St-Elsewhere from a nursing home, with decreased GCS. He was dehydrated and hypernatraemic, having stopped eating and drinking; he was not deemed (quite appropriately) a candidate for escalation of care but was admitted to the ward for IV fluids. He became restless and pulled out his lines; to me, admittedly an inexperienced junior, it seemed that the patient was dying but the consultant in charge insisted we sedate the patient and normalise the sodium. Over days, the lines went in, were pulled out, went in, were pulled out... You get the picture. Anyway, we managed to normalise the sodium but the patient didn't improve; he wouldn't eat or drink, couldn't communicate.. The sodium then went up again. Eventually we put him on the LCP and he died. I know this is not quite comparable to our patients and it's not a case of 'hospital-acquired' hypernatraemia but this paper made me think of it and to me it highlights the importance of thinking about the cause of deranged electrolytes, something that this paper doesn't really seem to address..

I think it would have been useful to know what sort of fluids the patients were getting/what the cause of hypernatraemia was felt to be in the hypernatraemia group, to know whether the authors suggestion of 'ICU-acquired' hypernatraemia being 'most often iatrogenic' was supported by their findings. The whole notion of 'ICU-acquired' conditions, as highlighted here, is one I find a bit difficult and I'm not sure what difference it makes - maybe someone more enlightened can help me? Of my ICCU experience to date, those that have gone on to develop AKI on the unit, for instance, have done so largely as a consequence of their underlying disease process and I can't accept 'ICU-acquired' as synonymous for poor care.

As for what constitutes 'good care'.. Ha, if only this was so easy! The GMC talk of 'keeping your knowledge and professional skills up to date', of 'safety and quality...communication, partnership and teamwork and maintaining trust'. Although we should endeavour to avoid harming our patients, electrolytes are surely only ever going to be part of a much bigger picture, of the individual patient in his own clinical context; is hypernatraemia a crime, if it gets someone off a ventilator to diurese them? Or does it just tell us that, irrespective of what we do, they are less likely to survive?

Reply

Deva

19/2/2014 02:37:59 pm

I started it, but I am no expert. Will give it a try...

In an RCT, random allocation ensures that treatment status will not be confounded by either measured or unmeasured baseline characteristics of patients. We all know that RCTs sometimes lack that robust external validity or real world application. I can quote the OSCILLATE and OSCAR as examples. The UK trial’s control arm is pragmatic, meaning manage the way you normally manage your patients by following best practice principles whereas the Canadian trial’s control arm is strictly protocolised, which may or may not have been the way of managing such patients before and after in (some of) the recruitment centres (I know of several ARDS patients not getting low Vt ventilation until prompted) . We don’t see that many pragmatic RCTs in the literature.
Observational studies, if done properly, have external validity because it is what you do normally, but lack that robust internal validity of RCTs. Statistical methods ‘attempt’ to compensate for some of the internal validity issues we see in observational studies. Bias, confounding, association but not causation are some of the main issues. Propensity scoring is used to perform ‘quasi’ randomization in observational studies to reduce the bias and confounding to some extent. It is used to analyse the causal relationship between the exposure and outcome. The other alternate is multivariate logistic regression analysis to adjust for the confounders.

Propensity scoring:
It is the likelihood that a person would have been treated using ONLY their measured covariate scores. It is a conditional probability. It does not control for unmeasured variables that may affect whether patients receive treatment, but one can measure the effect of unmeasured confounders from the goodness of fit. The literature describe it ‘a collection of covariates collapsed into a single variable, which is the probability of being treated’. It can be used for exposure to outcome studies like this one. You can read about the types of matching in the article written by Guyatt and Mebazza in ICM, August 2010.

Multivariate regression:
Commonly used to minimize the bias caused by measured confounders. It does not adjust for unmeasured confounders. Also, you cannot input a large number of variables into the model like you do in propensity model. The other issue to think about is whether the model used any interaction between variables (which is often not given in the paper). E.g. >80 years in an independent risk factor for higher mortality in ICU; having high sodium increases the risk of mortality separately. What if an 82 year old has high sodium – this is where ineraction is needed to arrive at a balanced estimate (1+1 ≠ 2).

Multivariate regression is the only option if most of the exposed individuals have high propensity score and non-exposed have low propensity. There is no way of matching them.

Don’t get me worng, propensity score is not the solution it is merely another option to consider. One of the authors of a paper I read warns that it is not a magic bullet to eliminate bias. It gives a more conservative estimate of the effect when compared to multivariate regression and we all like conservative estimates when it comes to outcomes because of the obvious issues surrounding observational studies. As Pete mentioned the sample size may go down with propensity scoring. For the evidence enthusiasts, a systematic review comparing propensity scores and regression did not find any difference.

Reply

Deva

24/2/2014 05:29:49 pm

Here are some useful video links covering the basics of confounding and regression. This guys channel is really good for basic understanding of study design, stats and appraisal.

http://www.youtube.com/watch?v=4s_QTi2AJ4k

http://www.youtube.com/watch?v=csDbNkP0ypg

http://www.youtube.com/watch?v=R6VwapsefRs

If the links don't work, just search Dr Terry Shaneyfelt's videos on You tube.